Object Detection Applications Research Articles

PENGEMBANGAN APLIKASI PENDETEKSI OBJEK UNTUK TUNANETRA MENGGUNAKAN OPERATOR TEPI CANNY DENGAN LIBRARY OPENCV BERBASIS ANDROID

<strong>Abstract: </strong><em>conducted by the Central Bureau of Statistics of East Java Province in 2019 stated that the number of blind people in the Pasuruan City/Regency was 273 people. From the results of the survey, the highest number of blind people is in the Pasuruan district. Smartphone and internet users in Indonesia by early childhood reached 29%. The use of android smartphones cannot be separated from human activities or activities during the Covid- 19 pandemic. The development of the Object Detection Application uses the waterfall method, in making the application using Android Studio with the Java programming language. By utilizing Canny Edge Operator Image Processing by optimizing the OpenCV Library, then using ML Kit to optimize object types</em>

Improved YOLOv5-Based Lightweight Object Detection Algorithm for People with Visual Impairment to Detect Buses

Object detection is crucial for individuals with visual impairment, especially when waiting for a bus. In this study, we propose a lightweight and highly accurate bus detection model based on an improved version of the YOLOv5 model. We propose integrating the GhostConv and C3Ghost Modules into the YOLOv5 network to reduce the number of parameters and floating-point operations per second (FLOPs), ensuring detection accuracy while reducing the model parameters. Following that, we added the SimSPPF module to replace the SPPF in the YOLOv5 backbone for increased computational efficiency and accurate object detection capabilities. Finally, we developed a Slim scale detection model by modifying the original YOLOv5 structure in order to make the model more efficient and faster, which is critical for real-time object detection applications. According to the experimental results, the Improved-YOLOv5 outperforms the original YOLOv5 in terms of the precision, recall, and mAP@0.5. Further analysis of the model complexity reveals that the Improved-YOLOv5 is more efficient due to fewer FLOPS, with fewer parameters, less memory usage, and faster inference time capabilities. The proposed model is smaller and more feasible to implement in resource-constrained mobile devices and a promising option for bus detection systems.

Object detection-oriented style transfer network for panchromatic remote sensing image

A panchromatic image is a remote sensing image that is imaged at the entire visible light band. It often has the highest spatial resolution and is widely used in the fields of resource detection, urban planning, geographic information systems, military, and national defense etc. However, the feature of single-band imaging determines that panchromatic images are usually displayed in the form of grayscale and result in some detailed differences between distinct types of ground objects that are indecipherable. It causes many difficulties for object detection applications. To address this problem, an object detection-oriented style transfer network for panchromatic remote sensing image is designed. In full consideration of the actual requirements of object detection tasks for panchromatic remote sensing image, a style transfer network based on feature fusion is designed, where a style transfer model is trained to transfer the grayscale panchromatic image into the corresponding color style. Further data preprocessing and postprocessing operations are designed to improve the quality of the transferred images and thus prevent negative transfer. DOTA dataset is used to verify the performance of the proposed algorithm. Results show that after the style transfer, the accuracy of object detection on the panchromatic remote sensing images has been significantly improved.

HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios, such as schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, including flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets, since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://pegasus.ac.cn.

On Development of Reliable Machine Learning Systems Based on Machine Error Tolerance of Input Images

With the rapid development of machine learning technologies, more and more practical applications arise. Representative machine learning techniques that receive much attention include object detection and image classification, which can be applied to many applications, such as self-driving cars, traffic flow calculation, and detection of product defects in factories. In this article, we investigate tolerability of errors for input images in machine learning systems and develop a generic reliability enhancement methodology. This work is based on our preliminary studies on image classification, but we put major focuses in object detection applications and the comprehensive comparisons to the prior studies. The first one error-tolerability test method to support reliability enhancement of object detection applications is then proposed based on careful error-tolerability examination of input images. The experimental results show that the test accuracy of this method can achieve 93.06%, which is the state-of-the-art. One special advantage of the proposed method is that unlike the previous error-tolerance methods in the literature, no golden reference data are required for acceptability determination by the proposed method. Hence, on-line testing can be supported. Our method is also implemented and validated in hardware. The results show that the hardware performance is up to 192 frames per second (FPS), which can thus also support real-time operations.

Eyes in Every Step: Navigating the Future with IoT and AI-ML in Smart Wearables for the Visually Impaired

The IoT-based Smart Shoe System revolutionizes independent navigation for the visually impaired through embedded ultrasonic sensors, an Arduino Nano board, and buzzer modules, enabling autonomous road traversal by detecting and alerting users to obstacles. In parallel, Smart Glasses explore the fusion of object detection and face recognition technologies to aid the visually impaired. Its object detection application assists in identifying obstacles, ensuring safer navigation, while the face recognition system, complemented by auditory output, facilitates the recognition of objects and individuals

A Universal Accelerated Coprocessor for Object Detection Based on RISC-V

With the application of object detection (OD) technology in various fields, common OD methods have been increasingly widely used, which poses a challenge to their calculative performance and accuracy. To improve the computational performance of OD algorithms, a general-purpose accelerated coprocessor was designed and implemented based on the extensibility of the RISC-V architecture. The accelerator was transformed into the form of a coprocessor and connected to its CPU core through the EAI coprocessor interface. Meanwhile, the corresponding instructions were designed, and the acceleration library functions were established. Finally, the performance and resource depletion of the coprocessor were analyzed on the FPGA platform. The experimental results suggest that only 10,091 LUTs were consumed by the accelerated coprocessor designed in this study, accounting for 52.2% of the entire SoC system. Moreover, the processing capacity of the YOLOv3 algorithm was 6.33 times that of a general-purpose CPU.

DRFnet: Dynamic receptive field network for object detection and image recognition.

Biological experiments discovered that the receptive field of neurons in the primary visual cortex of an animal's visual system is dynamic and capable of being altered by the sensory context. However, in a typical convolution neural network (CNN), a unit's response only comes from a fixed receptive field, which is generally determined by the preset kernel size in each layer. In this work, we simulate the dynamic receptive field mechanism in the biological visual system (BVS) for application in object detection and image recognition. We proposed a Dynamic Receptive Field module (DRF), which can realize the global information-guided responses under the premise of a slight increase in parameters and computational cost. Specifically, we design a transformer-style DRF module, which defines the correlation coefficient between two feature points by their relative distance. For an input feature map, we first divide the relative distance corresponding to different receptive field regions between the target feature point and its surrounding feature points into N different discrete levels. Then, a vector containing N different weights is automatically learned from the dataset and assigned to each feature point, according to the calculated discrete level that this feature point belongs. In this way, we achieve a correlation matrix primarily measuring the relationship between the target feature point and its surrounding feature points. The DRF-processed responses of each feature point are computed by multiplying its corresponding correlation matrix with the input feature map, which computationally equals to accomplish a weighted sum of all feature points exploiting the global and long-range information as the weight. Finally, by superimposing the local responses calculated by a traditional convolution layer with DRF responses, our proposed approach can integrate the rich context information among neighbors and the long-range dependencies of background into the feature maps. With the proposed DRF module, we achieved significant performance improvement on four benchmark datasets for both tasks of object detection and image recognition. Furthermore, we also proposed a new matching strategy that can improve the detection results of small targets compared with the traditional IOU-max matching strategy.

Fundamental limits to depth imaging with single-photon detector array sensors

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

Pavement disease detection and classification is one of the key problems in computer vision and intelligent analysis. This is an automated target detection technology with great development potential, which can improve the detection efficiency of road management departments. The research based on the convolutional neural network is aimed at realizing asphalt pavement disease detection based on low resolution, occlusive interference, and complex environment. Considering the powerful function of the convolutional neural network and its successful application in object detection, we apply it to asphalt pavement disease detection, and the detection results are used for subsequent analysis and decision‐making. At present, most of the research on pavement disease detection focuses on crack detection, and the detection of multiclass diseases is less, and its detection accuracy and speed need to be improved, which does not meet the actual engineering application. Therefore, a rapid asphalt pavement disease detection method based on improved YOLOv5s was proposed. The complex scene data enhancement technique was developed, which is used to enhance and extend the original data to improve the robustness of the model. The improved lightweight attention module SCBAM was integrated into the backbone network, which can enhance the feature extraction ability and improve the detection performance of the model for small targets. The spatial pyramid pooling was improved into SPPF to fuse the input features, which can solve the multiscale problem of the target and improve the reasoning efficiency of the model to a certain extent. The experimental results showed that, after the model is improved, the average accuracy of pavement disease reaches 94.0%. Compared with YOLOv5s, the precision of the improved YOLOv5‐pavement is increased by 3.1%, the recall rate is increased by 4.4%, the F1 score is increased by 3.7%, and the mAP is increased by 3.8%. For transverse cracks, longitudinal cracks, mesh cracks, potholes, and repaired pavement, the detection accuracy of pavement disease detection method based on YOLOv5‐pavement is improved by 3.4%, 3.1%, 4.0%, 7.5%, and 4.8%, respectively, compared with that based on YOLOv5s. The proposed method provides support for the detection work of pavement diseases.

An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes

The indoor scene object detection technology is of important research significance, which is one of the popular research topics in the field of scene understanding for indoor robots. In recent years, the solutions based on deep learning have achieved good results in object detection. However, there are still some problems to be further studied in indoor object detection methods, such as lighting problem and occlusion problem caused by the complexity of the indoor environment. Aiming at these problems, an improved object detection method based on deep neural networks is proposed in this paper, which uses a framework similar to the Single Shot MultiBox Detector (SSD). In the proposed method, an improved ResNet50 network is used to enhance the transmission of information, and the feature expression capability of the feature extraction network is improved. At the same time, a multi-scale contextual information extraction (MCIE) module is used to extract the contextual information of the indoor scene, so as to improve the indoor object detection effect. In addition, an improved dual-threshold non-maximum suppression (DT-NMS) algorithm is used to alleviate the occlusion problem in indoor scenes. Finally, the public dataset SUN2012 is further screened for the special application of indoor scene object detection, and the proposed method is tested on this dataset. The experimental results show that the mean average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) of the proposed method can reach 54.10%, which is higher than those of the state-of-the-art methods.

Progress in small object detection for remote sensing images

独特的拍摄视角和多变的成像高度使得遥感影像中包含大量尺寸极其有限的目标，如何准确有效地检测这些小目标对于构建智能的遥感图像解译系统至关重要。本文聚焦于遥感场景，对基于深度学习的小目标检测进行全面调研。首先，根据小目标的内在特质梳理了遥感影像小目标检测的3个主要挑战，包括特征表示瓶颈、前背景混淆以及回归分支敏感。其次，通过深入调研相关文献，全面回顾了基于深度学习的遥感影像小目标检测算法。选取3种代表性的遥感影像小目标检测任务，即光学遥感图像小目标检测、SAR图像小目标检测和红外图像小目标检测，系统性总结了3个领域内的代表性方法，并根据每种算法使用的技术思路进行分类阐述。再次，总结了遥感影像小目标检测常用的公开数据集，包括光学遥感图像、SAR图像及红外图像3种数据类型，借助于3种领域的代表性数据集SODA-A（small object detection datasets）、AIR-SARShip和NUAA-SIRST（Nanjing University of Aeronautics and Astronautics，single-frame infrared small target），进一步对主流的遥感影像目标检测算法在面对小目标时的性能表现进行横向对比及深入评估。最后，对遥感影像小目标检测的应用现状进行总结，并展望了遥感场景下小目标检测的发展趋势。;Remote sensing images are often captured from multiview and multiple altitudes, thereby comprising a mass of objects with limited sizes which significantly challenge current detection methods that can achieve outstanding performance on natural images. Moreover, how to precisely detect these small objects plays a crucial role in developing an intelligent interpretation system for remote sensing images. Focusing on the remote sensing images, this paper conducts a comprehensive survey for deep learning-based small object detection(SOD) can be reviewed and analyzed literately, including 1) features-represent bottlenecks, 2) background confusion, and 3) branching-regressed sensitivity. Specially, one of the major bottlenecks is for objective's representation. It refers that the down-sample operations in the prevailing feature extractors can suppress the signals of small objects unavoidably, and the following detection is impaired further in terms of the weak representations. The detection of size-limited instances is also interference of the confusion between the objects and backgrounds and the sensitivity of regression branch. For the former, the representations of small objects tend to be contaminated in related to feature extraction-contextual factors, which may erase the discriminative information that plays a significant role in head network. And the sensitivity of regression branch in small object detection is derived from the low tolerance for bounding box perturbation, in which a slight deviation of a predicted box will cause a drastic drop on the intersection-over-union(IoU), which is generally adopted to evaluate the accuracy of localization. Furthermore, we review and analyze the literature of small object detection for remote sensing images in the deep-learning era. In detail, by systematically reviewing corresponding methods of three small object detection tasks, i. e., SOD for optical remote sensing images, SOD for synthetic aperture radar(SAR) images and SOD for infrared images, an understandable taxonomy of the reviewed algorithms for each task is given. Specifically, we rigorously split the representative methods into several categories according to the major techniques used. In addition to the algorithm survey, considering the deep learning-based methods are hungry for data and to provide a comprehensive survey about small object detection, we also retrospect several publicly available datasets which are commonly used in these three SOD tasks. For each concrete field, we list the prevailing benchmarks in accordance with the published papers, and a brief introduction and some example images about these datasets are illustrated:small size. What is more, other related features about small object detection are highlighted as well, such as image resolution, data source, the number of images and annotated instances, and some proper statistics of each task, etc. Additionally, to better investigate the performance of generic detection methods on small objects, we analyze an in-depth evaluation and comparison of main-stream detection algorithms and several SOD methods for remote sensing images, namely SODA-A (small object detection datasets), AIR-SARShip and NUAA-SIRST (Nanjing University of Aeronautics and Astronautics, single-frame infrared small target). Afterwards, current situation in applications of small object detection for remote sensing images are analyzed, including SOD-based intelligent transportation system and scene-related understanding. Such harbor-targeted recognition is based on SAR image analysis, the precision-guided weapons based on the detection and recognition techniques of infrared images, and the tracking of moving targets at sea on top of multimodal remote sensing data. In the end, to enlighten the further research of small object detection in remote sensing images, we discuss four promising directions in the future. Concretely, it is required that an efficient backbone network can avoid the information loss of small objects while capturing the discriminative features to optimize the down-stream tasks about small objects. Large-scale benchmarks with well annotated small instances play an irreplaceable role linked to small object detection in remote sensing images further. Moreover, a multimodal remote sensing data-collaborated SOD algorithm is also preferred. A proper evaluation metric can not only guide the training and inference of small object detection methods under some specific scenes, but also rich its domain-related development.

Research of Simultaneous Defects Visualizing Algorithm for Both Macro and Micro Defects Based on Nonlinear Lamb Wave with an Application of Faster R-Cnn Object Detection

Research of Simultaneous Defects Visualizing Algorithm for Both Macro and Micro Defects Based on Nonlinear Lamb Wave with an Application of Faster R-Cnn Object Detection

A Visual Target Representation using Saliency Detection Approach

The task of saliency detection is to identify the most important and informative part of a scene. Saliency detection is broadly applied to numerous vision problems, including image segmentation, object recognition, image compression, content-based image retrieval, and moving object detection. Existing saliency detection methods suffer a low accuracy rate because of missing components of saliency regions. This study proposes a visual saliency detection method for the target representation to represent targets more accurately. The proposed method consists of five modules. In the first module, the salient region is extracted through manifold ranking on a graph, which incorporates local grouping cues and boundary priors. Secondly, using a region of interest (ROI) algorithm and the subtraction of the salient region from the original image, other parts of the image, either related or nonrelated to the interested target, are segmented. Lastly, those related and non-related regions are classified and distinguished using our proposed algorithm. Experimental result shows that proposed salient region accurately represent the interested target which can be used for object detection and tracking applications.

ECCVideo: A Scalable Edge Cloud Collaborative Video Analysis System

Video analysis drives a wide range of applications in the fields of public safety, autonomous vehicles, etc., with the great potential to impact society. Traditional cloud-based approaches are not applicable because of prohibitive bandwidth consumption and high response latency, while simply edge-based video analysis suffers from large computation delay, considering the restricted computing capacity of edge servers. Therefore, in this article, we focus on low-latency edge-cloud collaborative video analytic applications (ECCVApps) by making full use of resources at both the edge and cloud. Particularly, we present an edge-cloud collaborative video analysis system called ECCVideo, to support the unified management of heterogeneous servers and facilitate the development and deployment of large-scale ECCVApps. Under ECCVideo, we design the application architecture of ECCVApps, including presentation paradigm, transparent communication services, and full lifecycle management. To validate the proposed system, a real-time object detection application is deployed on the ECCVideo prototype.

A Reconfigurable CNN-Based Accelerator Design for Fast and Energy-Efficient Object Detection System on Mobile FPGA

In limited-resource edge computing circumstances such as on mobile devices, IoT devices, and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) accelerator implemented on mobile Field Programmable Gate Array (FPGA) is becoming more attractive due to its high accuracy and scalability. In recent days, mobile FPGAs such as the Xilinx PYNQ-Z1/Z2 and Ultra96, definitely have the advantage of scalability and flexibility for the implementation of deep learning algorithm-based object detection applications. It is also suitable for battery-powered systems, especially for drones and electric vehicles, to achieve energy efficiency in terms of power consumption and size aspect. However, it has the low and limited performance to achieve real-time processing. In this article, optimizing the accelerator design flow in the register-transfer level (RTL) will be introduced to achieve fast programming speed by applying low-power techniques on FPGA accelerator implementation. In general, most accelerator optimization techniques are conducted on the system level on the FPGA. In this article, we propose the reconfigurable accelerator design for a CNN-based object detection system on the register-transfer level on mobile FPGA. Furthermore, we present RTL optimization design techniques that will be applied such as various types of clock gating techniques to eliminate residual signals and to deactivate the unnecessarily active block. Based on the analysis of the CNN-based object detection architecture, we analyze and classify the common computing operation components from the Convolutional Neuron Network, such as multipliers and adders. We implement a multiplier/adder unit to a universal computing unit and modularize it to be suitable for a hierarchical structure of RTL code. The proposed system design was tested with Resnet-20 which has 23 layers and it was trained with the dataset, CIFAR-10 which provides a test set of 10,000 images in several formats, and the weight data we used for this experiment was provided from Tensil. Experimental results show that the proposed design process improves the power efficient consumption, hardware utilization, and throughput by 16%, up to 58%, and 15%, respectively.

Adaptive Weighted Flow Net Algorithm for Human Activity Recognition Using Depth Learned Features

Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection, modelling of individual frame characteristics, and communication among them. Moreover, this work focuses on finding out the human actions from various video resolutions, invariant human poses, and nearness of multi objects. First, we identify the key and essential frames of each activity using histogram differences. Secondly, Discrete Wavelet Transform (DWT) is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space. Finally, an Adaptive Weighted Flow Net (AWFN) algorithm is proposed in this work for effective video activity recognition. Moreover, the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group (VGG-16) convolution neural networks for making performance comparisons. This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy. The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51. From these investigations, it is proved that the proposed AWFN approach gives higher detection accuracy of 96%. It is approximately 0.3% to 7.88% of higher accuracy than state-of-art methods.

A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics

Detection of the object in the video is a primary task of all video-processing-based applications. It is one of the challenging areas in computer vision. The paper presents a novel MATLAB-based object detection application based on an improved Gaussian Mixture Model. Gaussian Mixture Model with post-processing applied here for segmentation of foreground from background. The application is divided into three modules pre-processing, detection and post-processing. The morphological gradient filter uses here for segmenting the foreground objects from the background. The proposed method was tested for various video sequences with challenges and proved that the approach performs well for the majority video sequences.

Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

The object of research is a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network. Within the recent few decades, fires are considered as one of the most serious disaster that occurs in many places around the world. The severity of fire incidents causes damages to buildings, infrastructures and properties. Resulting losses of human’s life and costs them a lot of losses. Thus, fire poses a great threat to us significantly; it is extremely dangerous for fire fighters. Fires can be resulted by materials such as rubber and chemical products. Other sources of fire are the short circuits on electrical devices and faults in power circuits. Additionally, overheating and overloading problems can be the cause of fire incidents. All these reasons lead to bad consequences when there is no immediate response to such problems. The advent of computer vision technology has played such a significant role for human life. Artificial intelligence field has improved the efficiency and behaviors of robotics beyond expectations. The interference of artificial intelligence made robotics act intelligently. For this reason, in this paper we presented a mobile robot based on deep learning to detect the fire source and determines its coordinate position then automatically moves toward the target and extinguish fire. Deep learning algorithms are the efficient ones for object detection applications. CNN model is one of the most common deep learning algorithms which have been used in the study for the fire detection. Due to the insufficient amount of datasets and large efforts required to build model from scratch. MobileNet V2 is one of the CNN models that support transfer learning technique. After training the model and testing it on 20 % of the used datasets the classification accuracy achieved up to 98.01 %. The motion repeatability of the robot has been implemented and tested resulting mean error 0.648 cm.

Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Light detection and ranging (LiDAR) sensors are increasing in popularity due to the advantages they provide over 2-D sensors in IoT object detection and classification applications, because of their ability to provide very precise distances to objects. Deep learning algorithms need a huge amount of data during training to obtain high accuracy results. When using 2-D images, a vast quantity of data sets are publicly available, but this is not the case for LiDAR point clouds. Each LiDAR model generates a point cloud with unique properties, which causes the data sets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs, it is necessary to generate the data sets manually. For this purpose, the data must be captured and then labeled one by one, which is a very time and cost-consuming process. To overcome this issue and to reduce the development time when using LiDAR sensors with deep learning algorithms, a methodology is proposed in this article to automatically generate point cloud data sets using a 3-D simulator for autonomous cars. In this regard, a data set can be generated for any LiDAR model by adding the specific LiDAR parameters to the simulator. Besides, custom scenarios can be designed and generated, based on the final deployment location, to provide a simulated solution very close to the final implementation. With the proposed methodology, a simulation can be performed to select the LiDAR that best fits certain application requirements, in contrast to the traditional approach where the LiDAR must first be purchased.